Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation

TL;DR

This paper introduces a Gaussian process-based latent-space interpolation method for non-intrusive reduced-order models, enabling continuous time evolution and spatial interpolation with quantified uncertainty, demonstrated on shallow water equations.

Contribution

It proposes a novel Gaussian process interpolation approach in latent space for non-intrusive ROMs, improving interpretability and enabling continuous time and space predictions.

Findings

Effective temporal interpolation with uncertainty quantification.

Improved spatial interpolation capabilities.

Validated on advection-dominated shallow water system.

Abstract

Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerging from various domain sciences. They provide a low-dimensional emulation framework for systems that may be intrinsically high-dimensional. This is accomplished by utilizing a construction algorithm that is purely data-driven. It is no surprise, therefore, that the algorithmic advances of machine learning have led to non-intrusive ROMs with greater accuracy and computational gains. However, in bypassing the utilization of an equation-based evolution, it is often seen that the interpretability of the ROM framework suffers. This becomes more problematic when black-box deep learning methods are used which are notorious for lacking robustness outside the physical regime of the observed data. In this article, we propose the use of a novel latent-space interpolation algorithm based on Gaussian process regression. Notably, this reduced-order evolution of the system is parameterized by control parameters to allow for interpolation in space. The use of this procedure also allows for a continuous interpretation of time which allows for temporal interpolation. The latter aspect provides information, with quantified uncertainty, about full-state evolution at a finer resolution than that utilized for training the ROMs. We assess the viability of this algorithm for an advection-dominated system given by the inviscid shallow water equations.